Agentic AI in Revenue Cycle Management: The Question Every Practice Should Ask Before Buying In

Walk a single aisle of HIMSS or read any RCM trade publication this year and one phrase dominates: agentic AI. Software agents that can complete revenue cycle tasks autonomously. Digital workers that handle prior authorizations, claim status checks, and denial appeals. Platforms that promise to take entire workflows off the human team’s plate.

The pitch is real. Some of the underlying technology is real too. But for practice managers and billing leaders evaluating these tools, there is one foundational question that gets buried under the demo videos: what does the AI actually run on?

What Agentic AI Means in RCM

Agentic AI describes software systems that can take a goal, plan a sequence of steps, execute those steps using available tools, and adjust based on results. In an RCM context, that often looks like an AI agent that can read a claim, check eligibility, identify a denial reason, draft an appeal, submit it, and track the outcome, all without a human walking it through each step.

When it works well, it is powerful. When it does not, the failures tend to be quiet, expensive, and hard to diagnose.

The Question Most Vendors Do Not Want You to Ask

Every agentic AI tool that touches the revenue cycle relies on the same underlying infrastructure that traditional RCM software has used for years. Claims still need to reach payers. Eligibility responses still come back through standard EDI channels. ERA files still need to be processed and posted accurately. Denial codes still arrive through 277 transactions.

In other words, the AI sits on top of clearinghouse and EDI infrastructure. It does not replace it.

The question to ask any agentic AI vendor is straightforward: what is your transaction infrastructure, and how reliable is it?

If the answer is vague, evasive, or quickly redirected to the AI capabilities, that is a flag worth paying attention to. An AI agent that submits 1,000 claims through an unreliable connection is just an automated way to generate 1,000 problems.

Why the Foundation Matters

Three common failure modes show up when AI is layered onto weak infrastructure:

• Silent transaction failures. Claims get queued, the AI agent reports completion, but the transaction never actually reached the payer. By the time the issue is caught, days or weeks of revenue are at risk.
• Eligibility data quality issues. AI agents make decisions based on the eligibility responses they receive. If the underlying eligibility verification is incomplete, stale, or wrong, the agent’s decisions inherit those errors at scale.
• Audit trail gaps. When something goes wrong with an AI-driven workflow, your team needs to be able to reconstruct what happened. If the underlying transaction logs are thin, troubleshooting becomes guesswork.

The point is not that AI in RCM is unreliable. The point is that AI is only as reliable as the layer it runs on.

What to Evaluate Before Adopting

When practices evaluate agentic AI tools, the conversation should include questions about the foundation as much as the features:

• What transaction infrastructure does the AI use? Is it built on a proven clearinghouse, or on something newer and untested?
• What is the underlying clean claim rate of the connections the AI relies on?
• How are eligibility responses sourced, and how current are they?
• What audit trail does the AI generate, and can your team review every action it takes on your behalf?
• How does the vendor handle errors, exceptions, and ambiguous cases? Is there a real human escalation path?
• What happens when the AI is wrong? Who owns the outcome, and how is the practice protected?

These questions do not undermine the value of AI. They protect it.

The Long-Term View

Agentic AI is going to keep accelerating. Practices that embrace it thoughtfully will see real productivity gains. Practices that adopt without scrutiny will spend the next few years cleaning up problems they did not see coming.

The vendors and platforms that win in the long run will be the ones with serious infrastructure underneath the AI layer. The clearinghouse and EDI fundamentals that have powered healthcare transactions for decades are not going away. They are becoming more important, not less.

How Harris Secure Connect Fits

Harris Secure Connect has been the connectivity layer for medical practices for more than 26 years. Our clearinghouse platform is built for reliability, transparency, and audit trail integrity. As AI tools enter the workflows our clients use, we make sure the foundation those tools depend on is solid.

If you are evaluating an agentic AI tool and want a clearer picture of what is running underneath the demo, our team is happy to help you ask the right questions.